Computers, Data and Big Information

In the first half of this unit students are introduced to the concepts of computers and computing while investigating the major components of computers and the suitability of these components for application and use. Fundamental notions of Human Computer Interaction (HCI) as well as looking past the individual bits and bytes of our binary code to how see how the data is collected and changed into information humans can understand and use. Students will learn that “intelligent” machine behavior is not “magic” but is based on algorithms applied to useful representations of information, including large data sets. Students will learn the characteristics that make certain tasks easy or difficult for computers, and how these differ from those that humans characteristically find easy or difficult.  

In the second half of the unit, Data and Society will cover the importance of data in solving problems and highlights how computers can help in this process. We explore the different systems and ways to use and represent information in a computer and the challenges and tradeoffs posed by using them. Students also learn how collections of data are used to solve problems, and how computers help to automate the steps of this process. Students will gain an appreciation for the many ways in which computing-enabled innovation has had an impact on society, as well as for the many different fields in which they are used. Connections among social, economic and cultural contexts will be discussed. Finally, this part of the unit further explores the ways that digital information is encoded, represented and manipulated. Being able to digitally manipulate data, visualize it, and identify patterns, trends and possible meanings are important practical skills that computer scientists do every day. Understanding where data comes from, having intuitions about what could be learned or extracted from it, and being able to use computational tools to manipulate data and communicate about it are the primary skills addressed in the part of the unit.

In the final project of the unit, student will use large data sets to analyze data and use it to develop insights and inferences found in the data. Students will use computers and software tools to translate, process and visualize data to find patterns and test their hypotheses. Working with a variety of large data sets students will illustrate how data and information facilitates identification of problems and solutions. Students will use data to discuss appropriate methods for data collection and aggregation of data necessary to support making a case or facilitating a discovery.

Unit objectives:

Students will be able to:

     • Choose the best way to represent some information based on how it will be used.
     • Use the problem-solving process to answer a question using data. 
     • Provide examples of how representing data in diverse ways can affect its ability to
        understood. 
     • Interpret collected data to identify patterns then use that data to draw conclusions. 
     • Give examples of how data is collected from sensors and for tracking proposes. 
     • Determine data that would be helpful in solving a problem or answering a question 
     • Identify and remove irrelevant data from a data set. 
     • Use tables and visualizations summarizing data to support a decision 
     • Present and critique interpretations of tables and visualizations 
     • Visually organize data to highlight relationships and support a claim. 
     • Use a tool that provides access to “big data” and investigate its sources.  
     • Provide plausible explanations of trends and patterns observed within a data visualization. 

The students will work independently and in small groups to learn, develop and practice their big data and information processing skills and knowledge to perform and complete various tasks to display and represent the data.

In this unit the students will use Microsoft Excel and other online data representation tools which will allow the students to create and visualize their data in real time, so they can focus more on the thought process, and understanding data to format the data and turn it into information that is clear and meaningful to humans.